The ZuSE-KI-Mobil (ZuKIMo) research project presents a heterogeneous system-on-chip (SoC) designed for use in a variety of automotive and industrial edge applications. Implemented using GlobalFoundries (GF) 22-nm FD-SOI technology, the SoC features a modular architecture with a configurable, bit-serial, mixed-precision neural processing unit (NPU) core. This core can be adapted to different use cases, comes with a compact instruction set, and improves the performance of dilated convolutions. A hardware-accelerated, tunable image signal processor (ISP) hyperparameter pipeline reduces tuning time and increases detection confidence for AI tasks. The system also incorporates a selective, per-layer fault-tolerance mechanism and supports rapid prototyping via an Apache TVM-driven compiler flow and cycle-accurate simulation. The adaptable hardware generation process is designed with future chiplet-based scaling in mind, providing a flexible foundation for upcoming heterogeneous SoC designs.